code: def test_evaluate(self):
      class mse(eg.Loss):
          def call(self, target, preds):
              return jnp.mean((target - preds) ** 2)
  
      class mae(eg.Metric):
          value: eg.MetricState = eg.MetricState.node(
              default=jnp.array(0.0, jnp.float32)
          )
  
          def update(self, target, preds):
              return jnp.mean(jnp.abs(target - preds))
  
          def compute(self) -> tp.Any:
              return self.value
  
      model = eg.Model(
          module=eg.Linear(1),
          loss=dict(a=mse()),
          metrics=dict(b=mae()),
          optimizer=optax.adamw(1e-3),
          eager=True,
      )
  
      X = np.random.uniform(size=(5, 2))
      y = np.random.uniform(size=(5, 1))
  
      logs = model.evaluate(x=X, y=y)
  
      assert "a/mse_loss" in logs
      assert "b/mae" in logs
      assert "loss" in logs
  
imports: import typing
  import unittest
  from dataclasses import dataclass
  from hashlib import new
  from pathlib import Path
  from tempfile import TemporaryDirectory
  import cloudpickle
  import jax
  import jax.numpy
  import numpy
  import optax
  import pytest
  import sh
  import tensorflow
  from treex.nn.linear import Linear
  import elegy
label_desc: Use image node_id 2 for calling the ModelBasicTest obj's underlying member method code with example usage: obj.test_evaluate() and returns: jnp, jnp, self